Book Image

Azure Data Scientist Associate Certification Guide

By : Andreas Botsikas, Michael Hlobil
Book Image

Azure Data Scientist Associate Certification Guide

By: Andreas Botsikas, Michael Hlobil

Overview of this book

The Azure Data Scientist Associate Certification Guide helps you acquire practical knowledge for machine learning experimentation on Azure. It covers everything you need to pass the DP-100 exam and become a certified Azure Data Scientist Associate. Starting with an introduction to data science, you'll learn the terminology that will be used throughout the book and then move on to the Azure Machine Learning (Azure ML) workspace. You'll discover the studio interface and manage various components, such as data stores and compute clusters. Next, the book focuses on no-code and low-code experimentation, and shows you how to use the Automated ML wizard to locate and deploy optimal models for your dataset. You'll also learn how to run end-to-end data science experiments using the designer provided in Azure ML Studio. You'll then explore the Azure ML Software Development Kit (SDK) for Python and advance to creating experiments and publishing models using code. The book also guides you in optimizing your model's hyperparameters using Hyperdrive before demonstrating how to use responsible AI tools to interpret and debug your models. Once you have a trained model, you'll learn to operationalize it for batch or real-time inferences and monitor it in production. By the end of this Azure certification study guide, you'll have gained the knowledge and the practical skills required to pass the DP-100 exam.
Table of Contents (17 chapters)
Section 1: Starting your cloud-based data science journey
Section 2: No code data science experimentation
Section 3: Advanced data science tooling and capabilities

Working in AzureML notebooks

AzureML Studio offers integration with a couple of code editors that allow you to edit notebooks and Python scripts. These editors are powered by the compute instance you provisioned in Chapter 4, Configuring the Workspace. If you have stopped that compute instance to save on costs, navigate to Manage | Compute and start it. From this view, you can open all third-party coding editors AzureML Studio integrates with, as shown in the following screenshot:

Figure 7.2 – List of third-party code editor experiences Azure Studio integrates with

The most widely known open source data science editors are Jupyter Notebook and its newer sibling, JupyterLab. You can open those editing environments by clicking on the respective links shown in the preceding screenshot. This will open a new browser tab, as shown in the following screenshot:

Figure 7.3 – JupyterLab and Jupyter editing experiences provided by the...